SDNC13 -Day2- The subjective science of persona building by Stephen Masiclat


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The subjective science of persona building by Stephen Masiclat - Syracuse University

Personas guide design and ensure services have a relevant constituency, but they have lacked a low-cost, scientifically valid method for genesis. This presentation shows how Q-Methodolgy defines rigorous personas to guide and test the service design process

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SDNC13 -Day2- The subjective science of persona building by Stephen Masiclat

  1. 1. service design research building better personas to guide design S.Masiclat The S.I.Newhouse School of Public Communications, Syracuse University SM(D) strategic media x design
  2. 2. the question a movie studio wants to know if a proposed new service has a market— if we build it, will they come? the proposed service is social+movie; is there an audience who wants to be social (i.e. tweet, text with friends, check in on Foursquare, etc) while they are watching a movie? the initial method was to form focus groups wherein we asked participants to react to a customer story. . .
  3. 3. the initial method was to form focus groups wherein we asked participants to react to a customer story. . . . . .this did not proceed as anticipated. . . in three attempts we never got past the initial phase of the customer story: “Imagine you’ve arrived at the multiplex to watch a movie. It’s one you’ve talked about with friends of yours, but they couldn’t make it with you to the theater this time. One of the options for the
  4. 4. starting over . . . is there a method that can: validate market sectors for an as yet undesigned service and define the personas that inhabit those sectors protect the privacy of the research subjects We did a literature review to see if anyone had published previous research in generating personas to guide design using a method that met the criteria above. . . One research finding stood out.
  5. 5. ‘Personas are bollocks*.’ “The most serious limitation of the Personas method is that it is difficult or impossible to verify that personas are accurate. This involves several aspects: a problematic relationship between personas and user populations; burdens on inference related to personas’ high specificity; and the possibility that personas are non-falsifiable.” Chapman, C.N, and Milham, R. P (2006) 'The persona's new clothes: methodological and practical arguments against a popular method' Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting, pp. 634 –636. Available online:
  6. 6. Q-methodology can address these issues Q is designed to study subjectivity scientifically: it captures a personal point of view it is arises from an internal frame of reference Q measures holistic attitudes, not bits and pieces overcomes inarticulateness by providing statements for sorting
  7. 7. Q-methodology was developed by William Stephenson PhD in physics (1926) and psychology (1929). Stephenson’s work in psychometrics advanced his advisor Charles Spearman’s work in factor analysis by applying eigenvector rotational maths to provide alternate views of data. Q analysis allows you to conduct intensive data analysis across multiple cases while respecting the captured contours of the data.
  8. 8. Q-Method steps: the Q-Sample Develop a concourse—a comprehensive set of statements— that capture the thoughts and opinions around a topic. For our study, these were sometimes speculative. From the concourse, select a representative Q-Sample of 3540 statements. 35 | I miss watching movies with my distant friends and family. 2 | “Checking in” to a show with an app makes watching TV a chore. 15 | I’m willing to see a movie I’m not interested in if I see it with friends. 18 | Using a mobile phone in a movie theater is rude! 20 | I enjoy being able to watch a movie whilst using my computer 19 | Watching a movie on a big screen is much better than on a tablet or mobile phone. 30 | People who tweet about the TV and movies they watch are narcissists. 12 | My family and I watch some TV shows ‘together’ even if we’re in different places. 6 | It ruins the movie experience when people talk or text in the theater. 11 | Sometimes, a moment in a movie makes me so excited that I have to tweet or share it. 1 | I like perfect silence when I’m watching a movie in a theater. 4 | My children like talking with their friends at the movie theater. 38 | Websites already spy on me, I don’t want my phone to do the same thing. 17 | My friends’ comments can make old movies new again. 34 | When I’m captivated by a movie, the last thing on my mind is sharing with my social network.
  9. 9. Q-Method steps: the preliminary Q-Sort At this stage, subjects sort the statements into three piles according to a condition of instruction. For our study, we asked people to sort the items from those they most agreed with, to those with which they most disagreed. Statements that do not elicit a strong opinion are left in the middle, “unsure” area. generally disagree generally agree leaning, but not sure 34 22 10 6 13 16 30 9 18 8 2 7 27 4 26 21 3 11 23 31 14 1 38 19 17 20 15 5 36 24 29 32 28 25 12 33 35 37
  10. 10. Q-Method steps: the final Q-Sort Now, subjects move to the positive pile, find the statements they are most strongly aligned to, and place those in the +4 category. Then they move to the negative pile, find the statements they most negatively react to, and place those in the -4 category. They repeat the process moving back and forth on the scale. -4 -3 -2 -1 +1 +2 0 +3 +4 5 22 2 24 12 6 30 15 18 14 10 23 27 32 38 11 3 21 31 7 1 16 19 25 28 34 33 29 4 9 8 13 35 20 17 36 26 37
  11. 11. Q-Method steps: factor analysis Using low-cost (and free) software Q-Sorts are inter-correlated and plotted. Individual plots are then rotated (the essential factor analysis technique). Researchers next apply rotation transformations (eigenvector varimax) to discern the factors.
  12. 12. Factors emerge and they describe a group’s shared ideas Q-Sorts that resemble each other closely coalesce into groups that share common attitudes. The method always seeks factor solutions that are most different from each other. Generally, only a few factor solutions emerge as the most distinct, most representative descriptions of the topic under study. The factors are the personas. Combined with traditional demographic data, you get a statistically valid descriptor of the different ideas people hold, and a beginning description of the people themselves.*
  13. 13. This is not a maths lecture. . . The keys for service design researchers are: This is a rigorous (scientific, falsifiable, repeatable, statistically valid, etc.) method that automatically generates personas based on a wholistic, shared mind-set. The findings emerge from the data. There is no a priori persona to which we try to align our services architecture, and around which we base our research. This method is low-cost; it is fast, you don’t need very large sample sizes, and the analysis tools are free or cheap. Most classically-educated executives are used to seeing largescale user data, or survey results from samples chosen for confidence interval. Therefore, you might still be asked to defend these results. . . mathematically.
  14. 14. The results are not random. In our movie service study, we had 38 statements. That means there are 38! (38 factorial) ways to arrange the statements. How big is 38 factorial ? number of ways to arrange divided by 9! 523,022,617,466,601,111,760,007,224,100,074,291,200,00 0,000 14,41,310,123,089,178,548,721,360,295,690,240,000,000
  15. 15. The results are not random. In our movie service study, we had 38 statements. That means there are 38! (38 factorial) ways to arrange the statements. How big is 38 factorial ? number of ways to arrange divided by 9! divided by 7,000,000,000 523,022,617,466,601,111,760,007,224,100,074,291,200,00 0,000 14,41,310,123,089,178,548,721,360,295,690,240,000,000 205,901,446,155,596,935,531,622,899,384 205 octillion, 901 septillion, 446 sextillion, 155 quintillion, 596 quadrillion, 935 trillion, 531 billion, 622 million, 899 thousand, 384 planet Earths to duplicate these results from random processes.
  16. 16. Our study yielded 3 factors—three personas with clear ideas about, and strong attitudes toward this proposed service. The vast majority (we estimate 70% of the general population) who demand absolute darkness and silence. The movie experience is about immersion, and any distraction is a diminution of the experience. Bros. A predominantly male group interested in social media-enabled connection around sports. For them, social media apps could preserve their friendly rivalries and increase the value of sports media. Young women who absolutely positively want the movie+social experience. Now. Like, seriously, where is this movie playing?
  17. 17. Our study done in June 2012. In September 2013, Disney announces their Second Screen Experience. images by Daniel Nasserian |
  18. 18. Design of the service proceeds from a clearly defined and validated persona images by Daniel Nasserian |
  19. 19. Thank you. Free Q-Method analysis software: International Society for the Scientific Study of Subjectivity (ISSSS) Big number calculations by Wolfram Alpha Stephen Masiclat Director, Graduate Program in New Media Management The S.I.Newhouse School of Public Communications Syracuse University SM(D) strategic media x design @masiclat